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use ai to write the script, but just got the result in the .txt
[36,10],[38,10],[37,13],[35,13]
there is "48" in the image, so how to get the finally result of "48"
//////////
import os
from surya.detection import batch_text_detection
from surya.model.detection.model import load_model, load_processor
from PIL import Image
def save_text_to_file(image_path, text):
# Get the file name and create the corresponding .txt file path
txt_file_path = f"{image_path.rsplit('.', 1)[0]}.txt"
with open(txt_file_path, 'w', encoding='utf-8') as f:
f.write(text)
def main(image_dir):
# Load model and processor
model, processor = load_model(), load_processor()
for filename in os.listdir(image_dir):
if not filename.endswith(".png"):
continue
image_path = os.path.join(image_dir, filename)
try:
img = read_image(image_path)
if img is None:
print(f"Skipping {image_path}")
continue
predictions = batch_text_detection([img], model=model, processor=processor)[0].bboxes
detected_texts = ""
for bbox in predictions:
# Here you would typically extract the actual text instead of polygon coordinates.
polygon_coords = str(bbox.polygon)[1:-1].replace(" ", "") + "\n"
detected_texts += polygon_coords
save_text_to_file(image_path, detected_texts)
print(f"Processed {filename}, saved text to {image_path.rsplit('.', 1)[0]}.txt")
except Exception as e:
print(f"Error processing {image_path}: {e}")
Example usage: Monitor images for automatic recognition and save to .txt files.
main(r"D:\Microsoft VS Code\py\social\ocr")
The text was updated successfully, but these errors were encountered:
use ai to write the script, but just got the result in the .txt
[36,10],[38,10],[37,13],[35,13]
there is "48" in the image, so how to get the finally result of "48"
//////////
import os
from surya.detection import batch_text_detection
from surya.model.detection.model import load_model, load_processor
from PIL import Image
def read_image(image_path):
try:
return Image.open(image_path)
except Exception as e:
print(f"Error opening image {image_path}: {e}")
return None
def save_text_to_file(image_path, text):
# Get the file name and create the corresponding .txt file path
txt_file_path = f"{image_path.rsplit('.', 1)[0]}.txt"
def main(image_dir):
# Load model and processor
model, processor = load_model(), load_processor()
Example usage: Monitor images for automatic recognition and save to .txt files.
main(r"D:\Microsoft VS Code\py\social\ocr")
The text was updated successfully, but these errors were encountered: